In today's business environment it is becoming increasingly difficult for each of us to stay informed. Each separate task might involve both collecting a number of documents and seeking information from relevant colleagues. The number of potentially relevant documents is vast, encompassing both those internal to an organization, and those available over public computer networks such as the World-Wide Web. In addition, in many organizations, the number of potentially relevant colleagues within the organization can be so large that an employee is unable to locate the best sources of knowledge without assistance.
Filtering systems exist which attempt to keep users informed by delivering relevant documents (e.g., Tak W. Yan and Hector Garcia-Molina, "SIFT--A Tool for Wide-Area Information Dissemination" Proc. of the 1995 USENIX Tech. Conf., pp 177-86, 1995). Unfortunately, these systems are based on preferences which need to be explicitly submitted by users. This is an onerous task. It is not always easy for users to clearly define their preferences nor to formulate them in a way that allows the computer system to make sense of them. Commonly used techniques require the user to specify a list of keywords denoting their interests, or to select from among a predetermined set of categories. Both of these requirements impose an additional workload upon the user.
Alternative systems exist which perform "collaborative filtering," for instance systems described in U.S. Pat. Nos. 4,996,642 (issued Feb. 26, 1991) and 5,583,763 (issued Dec. 10, 1996). In these systems, the user is required to denote a single set of favorite objects exemplifying their interests, or to supply preference scores for a number of objects. Once again, these are onerous tasks outside of the normal workload of a user. Furthermore, in many settings a user will have several different contexts for which they might require entirely different sets of recommendations. For instance, a user might be working on a number of projects simultaneously. The collaborative filtering systems referenced represent the user as having a monolithic set of interests and do not make different recommendations for different contexts.
There are also many varieties of "push" systems which do not attempt to target individual users or the specific interests of each user, but broadcast the same information objects to large segments of the user population. With this non-personalized approach, these systems end up adding to the problem of information overload rather than alleviating it.
Database systems exist which hold records of employee experiences, interests, skills, etc. These systems can be used to locate colleagues relevant to a particular task or project. Unfortunately, maintenance of such a database is expensive and difficult, and its use is not integrated into the employee's regular flow of work. In addition, these systems do not provide a single source for both relevant documents and relevant colleagues.